A universal strategy for the creation of machine learning-based atomistic force fields

Emerging machine learning(ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems.In this contribution,we outline a universal strategy to create ML-based atomistic force fields,which can be used to perform high-fidelity molecular dynamics simulations.This scheme involves(1)preparing a big reference dataset of atomic environments and forces with sufficiently low noise,e.g.,using density functional theory or higher-level methods,(2)utilizing a generalizable class of structural fingerprints for representing atomic environments,(3)optimally selecting diverse and nonredundant training datasets from the reference data,and(4)proposing various learning approaches to predict atomic forces directly(and rapidly)from atomic configurations.From the atomistic forces,accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory.Based on this strategy,we have created model ML force fields for six elemental bulk solids,including Al,Cu,Ti,W,Si,and C,and show that all of them can reach chemical accuracy.The proposed procedure is general and universal,in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention.Moreover,the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.

fficient and universal characterization of atomic structures through a topological graph order parameter

A graph-based order parameter,based on the topology of the graph itself,is introduced for the characterization of atomistic structures.The order parameter is universal to any material/chemical system and is transferable to all structural geometries.Four sets of data are used to validate both the generalizability and accuracy of the algorithm:(1)liquid lithium configurations spanning up to 300 GPa,(2)condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature,(3)a diverse set of aluminum configurations including surfaces,compressed and expanded lattices,point defects,grain boundaries,liquids,nanoparticles,all at nonzero temperatures,and(4)eleven niobium oxide crystal phases generated with ab initio molecular dynamics.We compare our proposed method to existing,state-of-the-art methods for the cases of aluminum and niobium oxide.Our order parameter uniquely classifies every configuration and outperforms all studied existing methods,opening the door for its use in a multitude of complex application spaces that can require fine structure-level characterization of atomistic graphs.

Ultrastructure of the gastro intestinal tract of healthy Japanese quail(Coturnix japonica) using light and scanning electron microscopy

The Japanese quail(Coturnix japonica) are popular both as an alternative protein source and as a model of choice for scientific research in several disciplines. There is limited published information on the histological features of the intestinal tract of Japanese quail. The only comprehensive reference is a book published in 1969. This study aims to fill that niche by providing a reference of general histological features of the Japanese quail, covering all the main sections of the intestinal tract. Both light and scanning electron microscope(SEM) images are presented. Results showed that the Japanese quail intestinal tract is very similar to that of the chicken with the exception of the luminal koilin membrane of the gizzard. Scanning electron microscopic photomicrographs show that in the Japanese quail koilin vertical rods are tightly packed together in a uniform manner making a carpet-like appearance. This differs in chicken where the conformations of vertical rods are arranged in clusters.